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Extending intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) for longitudinal data, with application to mental health trajectories in the UK

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  • Bell, Andrew

    (University of Sheffield)

  • Evans, Clare
  • Holman, Daniel
  • Leckie, George

Abstract

The intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach is gaining prominence in health sciences and beyond, as a robust quantitative method for identifying intersectional inequalities in a range of individual outcomes. However, it has so far not been applied to longitudinal data, despite the availability of such data, and growing recognition that intersectional social processes and determinants are not static, unchanging phenomena. Drawing on intersectionality and life-course theories, we develop a longitudinal version of the intersectional MAIHDA approach, allowing the analysis not just of intersectional inequalities in static individual differences, but also of life-course trajectories. We discuss the conceptualisation of intersectional groups in this context: how they are changeable over the life-course, appropriate treatment of generational differences, and relevance of the age-period-cohort identification problem. We illustrate the approach with a study of mental health using United Kingdom Household Longitudinal Study data. The results reveal important differences in trajectories between generations and intersectional strata, and show that trajectories are partly multiplicative but mostly additive in their intersectional inequalities. This article provides an important and much needed methodological contribution, enabling rigorous quantitative, longitudinal, intersectional analyses in social epidemiology and beyond.

Suggested Citation

  • Bell, Andrew & Evans, Clare & Holman, Daniel & Leckie, George, 2023. "Extending intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) for longitudinal data, with application to mental health trajectories in the UK," SocArXiv jq57s, Center for Open Science.
    • Handle: RePEc:osf:socarx:jq57s
    DOI: 10.31219/osf.io/jq57s
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    References listed on IDEAS

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    1. Blanchflower, David G. & Oswald, Andrew J., 2008. "Is well-being U-shaped over the life cycle?," Social Science & Medicine, Elsevier, vol. 66(8), pages 1733-1749, April.
    2. Daniel Holman & Alan Walker, 2021. "Understanding unequal ageing: towards a synthesis of intersectionality and life course analyses," European Journal of Ageing, Springer, vol. 18(2), pages 239-255, June.
    3. Evans, Clare R. & Erickson, Natasha, 2019. "Intersectionality and depression in adolescence and early adulthood: A MAIHDA analysis of the national longitudinal study of adolescent to adult health, 1995–2008," Social Science & Medicine, Elsevier, vol. 220(C), pages 1-11.
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    6. Alvarez, Camila H. & Evans, Clare Rosenfeld, 2021. "Intersectional environmental justice and population health inequalities: A novel approach," Social Science & Medicine, Elsevier, vol. 269(C).
    7. Zubizarreta, Dougie & Beccia, Ariel L. & Trinh, Mai-Han & Reynolds, Colleen A. & Reisner, Sari L. & Charlton, Brittany M., 2022. "Human papillomavirus vaccination disparities among U.S. college students: An intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)," Social Science & Medicine, Elsevier, vol. 301(C).
    8. Johannes Beller, 2022. "Age-period-cohort analysis of depression trends: are depressive symptoms increasing across generations in Germany?," European Journal of Ageing, Springer, vol. 19(4), pages 1493-1505, December.
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